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We present a workflow to track icebergs in proglacial fjords using oblique time-lapse photos and the Lucas-Kanade optical flow algorithm. We employ the workflow at LeConte Bay, Alaska, where we ran five time-lapse cameras between April 2016 and September 2017, capturing more than 400 000 photos at frame rates of 0.5–4.0 min−1. Hourly to daily average velocity fields in map coordinates illustrate dynamic currents in the bay, with dominant downfjord velocities (exceeding 0.5 m s−1 intermittently) and several eddies. Comparisons with simultaneous Acoustic Doppler Current Profiler (ADCP) measurements yield best agreement for the uppermost ADCP levels (~ 12 m and above), in line with prevalent small icebergs that trace near-surface currents. Tracking results from multiple cameras compare favorably, although cameras with lower frame rates (0.5 min−1) tend to underestimate high flow speeds. Tests to determine requisite temporal and spatial image resolution confirm the importance of high image frame rates, while spatial resolution is of secondary importance. Application of our procedure to other fjords will be successful if iceberg concentrations are high enough and if the camera frame rates are sufficiently rapid (at least 1 min−1 for conditions similar to LeConte Bay).

We present a detailed, complete glacier inventory for Alaska and neighboring Canada using multi-sensor satellite data from 2000 to 2011. For each glacier, we derive outlines and 51 variables, including center-line lengths, outline types and debris cover. We find 86 723 km2 of glacier area (27 109 glaciers >0.025 km2), ∼12% of the global glacierized area outside ice sheets. Of this area 12.0% is drained by 39 marine-terminating glaciers (74 km of tidewater margin), and 19.3% by 148 lake- and river-terminating glaciers (420 km of lake-/river margin). The overall debris cover is 11%, with considerable differences among regions, ranging from 1.4% in the Kenai Mountains to 28% in the Central Alaska Range. Comparison of outlines from different sources on >2500 km2 of glacierized area yields a total area difference of ∼10%, emphasizing the difficulties in accurately delineating debris-covered glaciers. Assuming fully correlated (systematic) errors, uncertainties in area reach 6% for all Alaska glaciers, but further analysis is needed to explore adequate error correlation scales. Preliminary analysis of the glacier database yields a new set of well-constrained area/length scaling parameters and shows good agreement between our area–altitude distributions and previously established synthetic hypsometries. The new glacier database will be valuable to further explore relations between glacier variables and glacier behavior.

Many glaciological and hydrological studies require outlines of individual glaciers rather than total ice cover. Here we develop a new semiautomatic algorithm that uses a digital elevation model (DEM) and outlines of glacier complexes to calculate the extents of individual glaciers. The algorithm first applies hydrological modeling tools to a modified DEM to calculate flowsheds. It then merges flowsheds that belong to individual glaciers using a distance-based approach, whose required empirical parameters are derived from the Juneau Icefield area in Alaska. In this region, 2% of ∼1300 glaciers were misclassified. The algorithm was validated on >25 000 km2 of ice in other regions in Alaska and on >40 000 km2 of ice in Arctic Canada, resulting in ∼2% and ∼3% misclassified glaciers, respectively. Results indicate that the algorithm is robust provided the DEM and the outlines are of good quality.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the ~198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726 800 ± 34 000 km2. The uncertainty, about ±5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

Spatial evolution of supraglacial debris cover on mountain glaciers is a largely unmonitored and poorly understood phenomenon that directly affects glacier melt. Supraglacial debris cover for 93 glaciers in the Karakoram, northern Pakistan, was mapped from Landsat imagery acquired in 1977, 1998, 2009 and 2014. Surge-type glaciers occupy 41% of the study area and were considered separately. The time series of debris-covered surface area change shows a mean value of zero or near-zero change for both surging and non-surging glaciers. An increase in debris-covered area is often associated with negative regional mass balances. We extend this logic to suggest that the stable regional mass balances in the Karakoram explain the zero or near-zero change in debris-covered area. This coupling of trends combined with our 37 year time series of data suggests the Karakoram anomaly extends further back in time than previously known.

We study the evolution of the Juneau Icefield, one of the largest icefields in North America (>3700 km2), using the Parallel Ice Sheet Model (PISM). We test two climate datasets: 20 km Weather Research and Forecasting Model (WRF) output, and data from the Scenarios Network for Alaska Planning (SNAP), derived from spatial interpolation of observations. Good agreement between simulated and observed surface mass balance was achieved only after substantially adjusting WRF precipitation to account for unresolved orographic effects, while SNAP's climate pattern is incompatible with observations of surface mass balance. Using the WRF data forced with the RCP6.0 emission scenario, the model projects a decrease in ice volume by 58–68% and a 57–63% area loss by 2099 compared with 2010. If the modeled 2070–99 climate is held constant beyond 2099, the icefield is eliminated by 2200. With constant 1971–2010 climate, the icefield stabilizes at 86% of its present-day volume. Experiments started from an ice-free state indicate that steady-state volumes are largely independent of the initial ice volume when forced by identical scenarios of climate stabilization. Despite large projected volume losses, the complex high-mountain topography makes the Juneau Icefield less susceptible to climate warming than low-lying Alaskan icefields.

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